544 Temporizing Matrix in the Complex Wound: A Retrospective Three-year Review
Bibliographic record
Abstract
Abstract Introduction A complex wound is a wound that will not heal spontaneously or with simple or standard closure techniques. Often functional structures (bone, tendon, fascia, joint capsule etc.) are exposed and a matrix can be used for bridging of these structures. The Temporizing Matrix is an entirely synthetic matrix made from polyurethane open-cell foam. This matrix was used in the burn center for three years for the indication “complex wound” with good success. The study objective was to evaluate success rate (leading to wound closure after STSG, duration of treatment) and complications (infection, failure, scarring) on this patient cohort. IRB approval was obtained. Methods All charts of patients receiving the Matrix between June 2017 through May 2020 were reviewed. Data collected were demographics, surgery dates, wound descriptions, healing, infection, failure, reapplication, time from application to STSG, time to healing, post discharge complications and scar quality. Results 33 patients with 37 complex wounds were identified to meet inclusion criteria, 61% male, 39% female, age ranging from 3 months to 72 years. The wounds were caused by Burns, necrotizing infections, trauma or amputation post burn. The Matrix was placed for widely exposed structures (70%), failed STSG(3%), thin subcutaneous tissue coverage over amputation stumps (15%) and other reasons (12%). Primary graft success was 97%. Infection rate was 15% with 8% reapplication. Most infections were treated locally. The average Vancouver scar scale rating after discharge was 9/15. Conclusions This temporizing Matrix in preparation to STSG led to successful wound closure in 97% of these complex wounds with low complication rates and an acceptable long-term scar.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".